Frontiers in plant science · 2026
Responses of soil carbon storage to tending thinning and rapid prediction based on NIRS-BPNN in larch-birch natural secondary forests.
СтатьяНазвание и аннотация: Перевод в очереди
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Meng Yongbin, Zhang Yuanyuan, Zhang Xiangqian, Jiang Wenting, Yang Yuanyuan, Chen Chunmei
Аннотация
INTRODUCTION: Heilongjiang Province ranks third in China in forest area, with over half of its forests being natural secondary forests. The province has fertile soil and plays a crucial role in China's forest ecosystem. Nevertheless, historical indiscriminate logging has resulted in degraded forest conditions, reduced productivity, and compromised carbon sequestration capacity.
METHODS: This research focused on the larch-birch natural secondary forest in the Greater Khingan Mountains, examining the soil twelve years after thinning. Six thinning treatments (0%, 16.7%, 25.5%, 34.4%, 49.6%, 59.9%) were compared to assess soil physical and chemical properties, soil carbon storage, and to elucidate the associations between thinning intensity and soil carbon storage dynamics. A soil organic carbon (SOC) content prediction model was developed using near-infrared spectroscopy and a Back Propagation neural network (BPNN) .
RESULTS: The findings indicate that thinning intensity significantly alters soil carbon storage by affecting key parameters such as soil moisture and organic carbon content. Specifically, a 34.4% thinning intensity significantly enhanced soil moisture content (56.67% vs. 33.02% in control, p < 0.05) and SOC content (66.17 g/kg vs. 32.03 g/kg in control, p < 0.05), leading to a substantial increase in total soil carbon storage (83.80 t/ha vs. 43.99 t/ha in control, p < 0.05). The NIRS-BPNN model achieved a prediction set correlation coefficient of Rp = 0.9293 (R2p = 0.8635, RMSEP = 0.0064, RPD = 2.8), demonstrating good predictive performance.
DISCUSSION: This research elucidates the responses of soil carbon storage to tending and thinning, providing a scientific foundation for the management of secondary forests. The NIRS-BPNN model offers a novel, rapid, and non-destructive approach for the large-scale assessment of SOC content in secondary forests, holding considerable theoretical and practical significance for forest carbon monitoring and climate change mitigation.
METHODS: This research focused on the larch-birch natural secondary forest in the Greater Khingan Mountains, examining the soil twelve years after thinning. Six thinning treatments (0%, 16.7%, 25.5%, 34.4%, 49.6%, 59.9%) were compared to assess soil physical and chemical properties, soil carbon storage, and to elucidate the associations between thinning intensity and soil carbon storage dynamics. A soil organic carbon (SOC) content prediction model was developed using near-infrared spectroscopy and a Back Propagation neural network (BPNN) .
RESULTS: The findings indicate that thinning intensity significantly alters soil carbon storage by affecting key parameters such as soil moisture and organic carbon content. Specifically, a 34.4% thinning intensity significantly enhanced soil moisture content (56.67% vs. 33.02% in control, p < 0.05) and SOC content (66.17 g/kg vs. 32.03 g/kg in control, p < 0.05), leading to a substantial increase in total soil carbon storage (83.80 t/ha vs. 43.99 t/ha in control, p < 0.05). The NIRS-BPNN model achieved a prediction set correlation coefficient of Rp = 0.9293 (R2p = 0.8635, RMSEP = 0.0064, RPD = 2.8), demonstrating good predictive performance.
DISCUSSION: This research elucidates the responses of soil carbon storage to tending and thinning, providing a scientific foundation for the management of secondary forests. The NIRS-BPNN model offers a novel, rapid, and non-destructive approach for the large-scale assessment of SOC content in secondary forests, holding considerable theoretical and practical significance for forest carbon monitoring and climate change mitigation.